Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations28236
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.4 MiB
Average record size in memory462.0 B

Variable types

Text2
DateTime1
Categorical6
Numeric12
Boolean3

Alerts

post_hour has constant value "9" Constant
caption_length is highly overall correlated with long_captionHigh correlation
engagement_rate is highly overall correlated with followers_count_before_post and 1 other fieldsHigh correlation
follower_bucket is highly overall correlated with followers_count_before_postHigh correlation
followers_count_before_post is highly overall correlated with engagement_rate and 1 other fieldsHigh correlation
has_high_hashtags is highly overall correlated with hashtags_countHigh correlation
hashtags_count is highly overall correlated with has_high_hashtagsHigh correlation
high_intent_engagement is highly overall correlated with savesHigh correlation
impressions is highly overall correlated with reachHigh correlation
is_weekend is highly overall correlated with post_dayHigh correlation
likes is highly overall correlated with engagement_rateHigh correlation
long_caption is highly overall correlated with caption_lengthHigh correlation
post_day is highly overall correlated with is_weekendHigh correlation
reach is highly overall correlated with impressionsHigh correlation
saves is highly overall correlated with high_intent_engagementHigh correlation
long_caption is highly imbalanced (63.9%) Imbalance
follower_bucket is uniformly distributed Uniform
post_id has unique values Unique
hashtags_count has 957 (3.4%) zeros Zeros

Reproduction

Analysis started2025-12-27 13:04:01.486163
Analysis finished2025-12-27 13:04:20.435753
Duration18.95 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

post_id
Text

Unique 

Distinct28236
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-12-27T18:34:20.722933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters254124
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28236 ?
Unique (%)100.0%

Sample

1st rowIG0013402
2nd rowIG0010751
3rd rowIG0013361
4th rowIG0027034
5th rowIG0022212
ValueCountFrequency (%)
ig0013402 1
 
< 0.1%
ig0011970 1
 
< 0.1%
ig0022212 1
 
< 0.1%
ig0027062 1
 
< 0.1%
ig0021589 1
 
< 0.1%
ig0012768 1
 
< 0.1%
ig0000281 1
 
< 0.1%
ig0013175 1
 
< 0.1%
ig0000512 1
 
< 0.1%
ig0021320 1
 
< 0.1%
Other values (28226) 28226
> 99.9%
2025-12-27T18:34:21.031415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 77131
30.4%
I 28236
 
11.1%
G 28236
 
11.1%
2 20749
 
8.2%
1 20711
 
8.1%
5 11315
 
4.5%
6 11313
 
4.5%
9 11293
 
4.4%
4 11289
 
4.4%
8 11286
 
4.4%
Other values (2) 22565
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 197652
77.8%
Uppercase Letter 56472
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77131
39.0%
2 20749
 
10.5%
1 20711
 
10.5%
5 11315
 
5.7%
6 11313
 
5.7%
9 11293
 
5.7%
4 11289
 
5.7%
8 11286
 
5.7%
3 11283
 
5.7%
7 11282
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
I 28236
50.0%
G 28236
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 197652
77.8%
Latin 56472
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77131
39.0%
2 20749
 
10.5%
1 20711
 
10.5%
5 11315
 
5.7%
6 11313
 
5.7%
9 11293
 
5.7%
4 11289
 
5.7%
8 11286
 
5.7%
3 11283
 
5.7%
7 11282
 
5.7%
Latin
ValueCountFrequency (%)
I 28236
50.0%
G 28236
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 254124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77131
30.4%
I 28236
 
11.1%
G 28236
 
11.1%
2 20749
 
8.2%
1 20711
 
8.1%
5 11315
 
4.5%
6 11313
 
4.5%
9 11293
 
4.4%
4 11289
 
4.4%
8 11286
 
4.4%
Other values (2) 22565
 
8.9%
Distinct366
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size441.2 KiB
Minimum2024-11-19 09:25:22.954916
Maximum2025-11-19 09:25:22.954916
Invalid dates0
Invalid dates (%)0.0%
2025-12-27T18:34:21.136908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:21.259533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

media_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Reel
7085 
Carousel
7082 
Video
7062 
Photo
7007 

Length

Max length8
Median length5
Mean length5.5015229
Min length4

Characters and Unicode

Total characters155341
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReel
2nd rowVideo
3rd rowVideo
4th rowVideo
5th rowCarousel

Common Values

ValueCountFrequency (%)
Reel 7085
25.1%
Carousel 7082
25.1%
Video 7062
25.0%
Photo 7007
24.8%

Length

2025-12-27T18:34:21.360315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T18:34:21.433173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
reel 7085
25.1%
carousel 7082
25.1%
video 7062
25.0%
photo 7007
24.8%

Most occurring characters

ValueCountFrequency (%)
e 28314
18.2%
o 28158
18.1%
l 14167
9.1%
R 7085
 
4.6%
C 7082
 
4.6%
a 7082
 
4.6%
r 7082
 
4.6%
u 7082
 
4.6%
s 7082
 
4.6%
V 7062
 
4.5%
Other values (5) 35145
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127105
81.8%
Uppercase Letter 28236
 
18.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28314
22.3%
o 28158
22.2%
l 14167
11.1%
a 7082
 
5.6%
r 7082
 
5.6%
u 7082
 
5.6%
s 7082
 
5.6%
i 7062
 
5.6%
d 7062
 
5.6%
h 7007
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
R 7085
25.1%
C 7082
25.1%
V 7062
25.0%
P 7007
24.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 155341
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28314
18.2%
o 28158
18.1%
l 14167
9.1%
R 7085
 
4.6%
C 7082
 
4.6%
a 7082
 
4.6%
r 7082
 
4.6%
u 7082
 
4.6%
s 7082
 
4.6%
V 7062
 
4.5%
Other values (5) 35145
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 28314
18.2%
o 28158
18.1%
l 14167
9.1%
R 7085
 
4.6%
C 7082
 
4.6%
a 7082
 
4.6%
r 7082
 
4.6%
u 7082
 
4.6%
s 7082
 
4.6%
V 7062
 
4.5%
Other values (5) 35145
22.6%

likes
Real number (ℝ)

High correlation 

Distinct26335
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98181.831
Minimum7
Maximum200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:21.537434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9485.5
Q148112.25
median96940.5
Q3147928.75
95-th percentile189716.5
Maximum200000
Range199993
Interquartile range (IQR)99816.5

Descriptive statistics

Standard deviation57878.895
Coefficient of variation (CV)0.58950719
Kurtosis-1.1981412
Mean98181.831
Median Absolute Deviation (MAD)49814.5
Skewness0.048168889
Sum2.7722622 × 109
Variance3.3499665 × 109
MonotonicityNot monotonic
2025-12-27T18:34:21.635289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82010 4
 
< 0.1%
194623 4
 
< 0.1%
61217 4
 
< 0.1%
86010 4
 
< 0.1%
18648 4
 
< 0.1%
97189 4
 
< 0.1%
4149 4
 
< 0.1%
31311 3
 
< 0.1%
60271 3
 
< 0.1%
29919 3
 
< 0.1%
Other values (26325) 28199
99.9%
ValueCountFrequency (%)
7 1
< 0.1%
9 2
< 0.1%
42 1
< 0.1%
53 1
< 0.1%
66 1
< 0.1%
67 1
< 0.1%
77 1
< 0.1%
95 1
< 0.1%
104 1
< 0.1%
108 1
< 0.1%
ValueCountFrequency (%)
200000 1
< 0.1%
199994 1
< 0.1%
199980 1
< 0.1%
199979 1
< 0.1%
199973 1
< 0.1%
199961 1
< 0.1%
199960 1
< 0.1%
199957 1
< 0.1%
199953 1
< 0.1%
199952 1
< 0.1%

comments
Real number (ℝ)

Distinct9416
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5014.3199
Minimum0
Maximum10000
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:21.744368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile499
Q12522.75
median5035
Q37520
95-th percentile9499.25
Maximum10000
Range10000
Interquartile range (IQR)4997.25

Descriptive statistics

Standard deviation2890.6387
Coefficient of variation (CV)0.57647672
Kurtosis-1.2021642
Mean5014.3199
Median Absolute Deviation (MAD)2497
Skewness-0.0078979804
Sum1.4158434 × 108
Variance8355791.9
MonotonicityNot monotonic
2025-12-27T18:34:22.155913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8617 10
 
< 0.1%
2643 9
 
< 0.1%
1458 9
 
< 0.1%
1961 9
 
< 0.1%
2990 9
 
< 0.1%
5857 9
 
< 0.1%
4407 9
 
< 0.1%
2982 9
 
< 0.1%
4888 9
 
< 0.1%
6357 9
 
< 0.1%
Other values (9406) 28145
99.7%
ValueCountFrequency (%)
0 3
< 0.1%
1 2
 
< 0.1%
2 3
< 0.1%
3 3
< 0.1%
4 2
 
< 0.1%
5 5
< 0.1%
6 5
< 0.1%
7 1
 
< 0.1%
8 3
< 0.1%
9 3
< 0.1%
ValueCountFrequency (%)
10000 2
 
< 0.1%
9999 4
< 0.1%
9998 5
< 0.1%
9997 2
 
< 0.1%
9996 2
 
< 0.1%
9993 1
 
< 0.1%
9992 3
< 0.1%
9990 3
< 0.1%
9989 3
< 0.1%
9988 2
 
< 0.1%

shares
Real number (ℝ)

Distinct4986
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2499.0101
Minimum0
Maximum5000
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:22.583877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile253
Q11242
median2492
Q33765.25
95-th percentile4757.25
Maximum5000
Range5000
Interquartile range (IQR)2523.25

Descriptive statistics

Standard deviation1448.8879
Coefficient of variation (CV)0.57978475
Kurtosis-1.2132385
Mean2499.0101
Median Absolute Deviation (MAD)1259.5
Skewness0.0085271807
Sum70562048
Variance2099276.2
MonotonicityNot monotonic
2025-12-27T18:34:23.091472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4820 16
 
0.1%
1444 16
 
0.1%
4520 15
 
0.1%
714 15
 
0.1%
823 14
 
< 0.1%
433 14
 
< 0.1%
558 14
 
< 0.1%
3932 14
 
< 0.1%
309 14
 
< 0.1%
3675 13
 
< 0.1%
Other values (4976) 28091
99.5%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 5
< 0.1%
2 3
 
< 0.1%
3 4
< 0.1%
4 8
< 0.1%
5 8
< 0.1%
6 9
< 0.1%
7 3
 
< 0.1%
8 4
< 0.1%
9 6
< 0.1%
ValueCountFrequency (%)
5000 9
< 0.1%
4999 8
< 0.1%
4998 10
< 0.1%
4997 4
 
< 0.1%
4996 11
< 0.1%
4995 5
< 0.1%
4994 5
< 0.1%
4993 8
< 0.1%
4992 4
 
< 0.1%
4991 9
< 0.1%

saves
Real number (ℝ)

High correlation 

Distinct12667
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7478.1542
Minimum0
Maximum15000
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:23.406173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile746.5
Q13671.75
median7467
Q311282.25
95-th percentile14236.5
Maximum15000
Range15000
Interquartile range (IQR)7610.5

Descriptive statistics

Standard deviation4353.7352
Coefficient of variation (CV)0.58219383
Kurtosis-1.2199524
Mean7478.1542
Median Absolute Deviation (MAD)3804
Skewness0.0063909675
Sum2.1115316 × 108
Variance18955010
MonotonicityNot monotonic
2025-12-27T18:34:23.619490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12676 9
 
< 0.1%
5574 8
 
< 0.1%
6181 8
 
< 0.1%
11330 8
 
< 0.1%
926 8
 
< 0.1%
13887 8
 
< 0.1%
10035 8
 
< 0.1%
6103 7
 
< 0.1%
3385 7
 
< 0.1%
1789 7
 
< 0.1%
Other values (12657) 28158
99.7%
ValueCountFrequency (%)
0 3
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 2
< 0.1%
4 4
< 0.1%
5 1
 
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 4
< 0.1%
ValueCountFrequency (%)
15000 2
< 0.1%
14999 4
< 0.1%
14998 2
< 0.1%
14997 2
< 0.1%
14996 3
< 0.1%
14995 2
< 0.1%
14994 2
< 0.1%
14993 1
 
< 0.1%
14992 4
< 0.1%
14991 1
 
< 0.1%

reach
Real number (ℝ)

High correlation 

Distinct28036
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1054345.4
Minimum20913
Maximum1999865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:23.823159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20913
5-th percentile211500.25
Q1580626.5
median1051291
Q31525056.2
95-th percentile1908777.5
Maximum1999865
Range1978952
Interquartile range (IQR)944429.75

Descriptive statistics

Standard deviation545631.27
Coefficient of variation (CV)0.51750713
Kurtosis-1.1863466
Mean1054345.4
Median Absolute Deviation (MAD)472670
Skewness0.0033196577
Sum2.9770497 × 1010
Variance2.9771348 × 1011
MonotonicityNot monotonic
2025-12-27T18:34:24.217809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
834459 3
 
< 0.1%
791989 2
 
< 0.1%
939058 2
 
< 0.1%
1621321 2
 
< 0.1%
1359807 2
 
< 0.1%
1611024 2
 
< 0.1%
145521 2
 
< 0.1%
961658 2
 
< 0.1%
1427413 2
 
< 0.1%
1944126 2
 
< 0.1%
Other values (28026) 28215
99.9%
ValueCountFrequency (%)
20913 1
< 0.1%
29995 1
< 0.1%
30314 1
< 0.1%
30608 1
< 0.1%
30615 1
< 0.1%
30902 1
< 0.1%
32483 1
< 0.1%
33270 1
< 0.1%
33274 1
< 0.1%
33541 1
< 0.1%
ValueCountFrequency (%)
1999865 1
< 0.1%
1999780 1
< 0.1%
1999750 1
< 0.1%
1999744 1
< 0.1%
1999631 1
< 0.1%
1999576 1
< 0.1%
1999543 1
< 0.1%
1999487 1
< 0.1%
1999431 1
< 0.1%
1999305 1
< 0.1%

impressions
Real number (ℝ)

High correlation 

Distinct28035
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1304714.6
Minimum31181
Maximum2497940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:24.851418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31181
5-th percentile415675.75
Q1832400.25
median1299030.5
Q31776869.2
95-th percentile2197843.8
Maximum2497940
Range2466759
Interquartile range (IQR)944469

Descriptive statistics

Standard deviation565667.54
Coefficient of variation (CV)0.43355655
Kurtosis-1.0416471
Mean1304714.6
Median Absolute Deviation (MAD)472828
Skewness0.006150288
Sum3.683992 × 1010
Variance3.1997977 × 1011
MonotonicityNot monotonic
2025-12-27T18:34:25.348137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1589220 3
 
< 0.1%
840985 3
 
< 0.1%
1936727 3
 
< 0.1%
977749 3
 
< 0.1%
1340259 2
 
< 0.1%
1874618 2
 
< 0.1%
2047351 2
 
< 0.1%
2063355 2
 
< 0.1%
655946 2
 
< 0.1%
906928 2
 
< 0.1%
Other values (28025) 28212
99.9%
ValueCountFrequency (%)
31181 1
< 0.1%
43717 1
< 0.1%
48060 1
< 0.1%
73729 1
< 0.1%
75094 1
< 0.1%
78076 1
< 0.1%
81868 1
< 0.1%
86152 1
< 0.1%
87380 1
< 0.1%
92094 1
< 0.1%
ValueCountFrequency (%)
2497940 1
< 0.1%
2489252 1
< 0.1%
2485727 1
< 0.1%
2484270 1
< 0.1%
2482212 1
< 0.1%
2480980 1
< 0.1%
2477184 1
< 0.1%
2474793 1
< 0.1%
2473379 1
< 0.1%
2473253 1
< 0.1%

caption_length
Real number (ℝ)

High correlation 

Distinct2201
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1102.4585
Minimum0
Maximum2200
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:26.017837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile107
Q1555
median1099.5
Q31655
95-th percentile2091
Maximum2200
Range2200
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation635.53687
Coefficient of variation (CV)0.5764724
Kurtosis-1.1989016
Mean1102.4585
Median Absolute Deviation (MAD)550.5
Skewness-0.0029517995
Sum31129017
Variance403907.12
MonotonicityNot monotonic
2025-12-27T18:34:26.218077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1787 28
 
0.1%
1959 25
 
0.1%
572 25
 
0.1%
384 25
 
0.1%
1673 25
 
0.1%
76 24
 
0.1%
449 24
 
0.1%
1034 24
 
0.1%
592 24
 
0.1%
1122 23
 
0.1%
Other values (2191) 27989
99.1%
ValueCountFrequency (%)
0 15
0.1%
1 14
< 0.1%
2 15
0.1%
3 16
0.1%
4 12
< 0.1%
5 9
< 0.1%
6 18
0.1%
7 11
< 0.1%
8 11
< 0.1%
9 9
< 0.1%
ValueCountFrequency (%)
2200 13
< 0.1%
2199 13
< 0.1%
2198 9
< 0.1%
2197 18
0.1%
2196 9
< 0.1%
2195 8
< 0.1%
2194 11
< 0.1%
2193 9
< 0.1%
2192 13
< 0.1%
2191 14
< 0.1%

hashtags_count
Real number (ℝ)

High correlation  Zeros 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.890849
Minimum0
Maximum30
Zeros957
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:26.347156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median15
Q323
95-th percentile29
Maximum30
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.001764
Coefficient of variation (CV)0.60451652
Kurtosis-1.2158726
Mean14.890849
Median Absolute Deviation (MAD)8
Skewness0.018252039
Sum420458
Variance81.031755
MonotonicityNot monotonic
2025-12-27T18:34:26.456464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
25 966
 
3.4%
0 957
 
3.4%
29 953
 
3.4%
5 950
 
3.4%
1 942
 
3.3%
6 936
 
3.3%
2 936
 
3.3%
28 936
 
3.3%
22 934
 
3.3%
3 928
 
3.3%
Other values (21) 18798
66.6%
ValueCountFrequency (%)
0 957
3.4%
1 942
3.3%
2 936
3.3%
3 928
3.3%
4 917
3.2%
5 950
3.4%
6 936
3.3%
7 916
3.2%
8 917
3.2%
9 920
3.3%
ValueCountFrequency (%)
30 899
3.2%
29 953
3.4%
28 936
3.3%
27 878
3.1%
26 874
3.1%
25 966
3.4%
24 879
3.1%
23 867
3.1%
22 934
3.3%
21 829
2.9%

followers_gained
Real number (ℝ)

Distinct1001
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.66189
Minimum0
Maximum1000
Zeros24
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:26.584342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1250
median500
Q3755
95-th percentile951
Maximum1000
Range1000
Interquartile range (IQR)505

Descriptive statistics

Standard deviation290.43177
Coefficient of variation (CV)0.57893927
Kurtosis-1.2149113
Mean501.66189
Median Absolute Deviation (MAD)252.5
Skewness-0.0031426126
Sum14164925
Variance84350.611
MonotonicityNot monotonic
2025-12-27T18:34:26.692841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
752 46
 
0.2%
398 46
 
0.2%
138 44
 
0.2%
470 43
 
0.2%
328 43
 
0.2%
841 43
 
0.2%
951 43
 
0.2%
667 43
 
0.2%
918 43
 
0.2%
985 42
 
0.1%
Other values (991) 27800
98.5%
ValueCountFrequency (%)
0 24
0.1%
1 20
0.1%
2 26
0.1%
3 31
0.1%
4 29
0.1%
5 22
0.1%
6 23
0.1%
7 27
0.1%
8 34
0.1%
9 32
0.1%
ValueCountFrequency (%)
1000 29
0.1%
999 31
0.1%
998 26
0.1%
997 34
0.1%
996 29
0.1%
995 24
0.1%
994 30
0.1%
993 18
0.1%
992 35
0.1%
991 21
0.1%

traffic_source
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Home Feed
4773 
Hashtags
4752 
Reels Feed
4730 
External
4713 
Profile
4695 

Length

Max length10
Median length9
Mean length8.1758394
Min length7

Characters and Unicode

Total characters230853
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReels Feed
2nd rowHashtags
3rd rowHashtags
4th rowExplore
5th rowProfile

Common Values

ValueCountFrequency (%)
Home Feed 4773
16.9%
Hashtags 4752
16.8%
Reels Feed 4730
16.8%
External 4713
16.7%
Profile 4695
16.6%
Explore 4573
16.2%

Length

2025-12-27T18:34:26.795089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T18:34:26.877843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
feed 9503
25.2%
home 4773
12.6%
hashtags 4752
12.6%
reels 4730
12.5%
external 4713
12.5%
profile 4695
12.4%
explore 4573
12.1%

Most occurring characters

ValueCountFrequency (%)
e 47220
20.5%
l 18711
 
8.1%
s 14234
 
6.2%
a 14217
 
6.2%
o 14041
 
6.1%
r 13981
 
6.1%
H 9525
 
4.1%
9503
 
4.1%
F 9503
 
4.1%
d 9503
 
4.1%
Other values (12) 70415
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 183611
79.5%
Uppercase Letter 37739
 
16.3%
Space Separator 9503
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 47220
25.7%
l 18711
 
10.2%
s 14234
 
7.8%
a 14217
 
7.7%
o 14041
 
7.6%
r 13981
 
7.6%
d 9503
 
5.2%
t 9465
 
5.2%
x 9286
 
5.1%
m 4773
 
2.6%
Other values (6) 28180
15.3%
Uppercase Letter
ValueCountFrequency (%)
H 9525
25.2%
F 9503
25.2%
E 9286
24.6%
R 4730
12.5%
P 4695
12.4%
Space Separator
ValueCountFrequency (%)
9503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 221350
95.9%
Common 9503
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 47220
21.3%
l 18711
 
8.5%
s 14234
 
6.4%
a 14217
 
6.4%
o 14041
 
6.3%
r 13981
 
6.3%
H 9525
 
4.3%
F 9503
 
4.3%
d 9503
 
4.3%
t 9465
 
4.3%
Other values (11) 60950
27.5%
Common
ValueCountFrequency (%)
9503
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 47220
20.5%
l 18711
 
8.1%
s 14234
 
6.2%
a 14217
 
6.2%
o 14041
 
6.1%
r 13981
 
6.1%
H 9525
 
4.1%
9503
 
4.1%
F 9503
 
4.1%
d 9503
 
4.1%
Other values (12) 70415
30.5%

content_category
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Fashion
2871 
Technology
2860 
Food
2842 
Photography
2837 
Lifestyle
2830 
Other values (5)
13996 

Length

Max length11
Median length9
Mean length7.108089
Min length4

Characters and Unicode

Total characters200704
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTechnology
2nd rowPhotography
3rd rowFashion
4th rowTechnology
5th rowPhotography

Common Values

ValueCountFrequency (%)
Fashion 2871
10.2%
Technology 2860
10.1%
Food 2842
10.1%
Photography 2837
10.0%
Lifestyle 2830
10.0%
Music 2825
10.0%
Fitness 2811
10.0%
Travel 2807
9.9%
Comedy 2780
9.8%
Beauty 2773
9.8%

Length

2025-12-27T18:34:26.984847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T18:34:27.183618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fashion 2871
10.2%
technology 2860
10.1%
food 2842
10.1%
photography 2837
10.0%
lifestyle 2830
10.0%
music 2825
10.0%
fitness 2811
10.0%
travel 2807
9.9%
comedy 2780
9.8%
beauty 2773
9.8%

Most occurring characters

ValueCountFrequency (%)
o 22729
 
11.3%
e 19691
 
9.8%
s 14148
 
7.0%
y 14080
 
7.0%
h 11405
 
5.7%
i 11337
 
5.6%
a 11288
 
5.6%
t 11251
 
5.6%
n 8542
 
4.3%
F 8524
 
4.2%
Other values (16) 67709
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 172468
85.9%
Uppercase Letter 28236
 
14.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 22729
13.2%
e 19691
11.4%
s 14148
 
8.2%
y 14080
 
8.2%
h 11405
 
6.6%
i 11337
 
6.6%
a 11288
 
6.5%
t 11251
 
6.5%
n 8542
 
5.0%
l 8497
 
4.9%
Other values (9) 39500
22.9%
Uppercase Letter
ValueCountFrequency (%)
F 8524
30.2%
T 5667
20.1%
P 2837
 
10.0%
L 2830
 
10.0%
M 2825
 
10.0%
C 2780
 
9.8%
B 2773
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 200704
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 22729
 
11.3%
e 19691
 
9.8%
s 14148
 
7.0%
y 14080
 
7.0%
h 11405
 
5.7%
i 11337
 
5.6%
a 11288
 
5.6%
t 11251
 
5.6%
n 8542
 
4.3%
F 8524
 
4.2%
Other values (16) 67709
33.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 22729
 
11.3%
e 19691
 
9.8%
s 14148
 
7.0%
y 14080
 
7.0%
h 11405
 
5.7%
i 11337
 
5.6%
a 11288
 
5.6%
t 11251
 
5.6%
n 8542
 
4.3%
F 8524
 
4.2%
Other values (16) 67709
33.7%
Distinct300
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-12-27T18:34:27.450705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters310596
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcreator_135
2nd rowcreator_109
3rd rowcreator_135
4th rowcreator_271
5th rowcreator_223
ValueCountFrequency (%)
creator_152 114
 
0.4%
creator_076 114
 
0.4%
creator_267 114
 
0.4%
creator_195 113
 
0.4%
creator_171 113
 
0.4%
creator_020 113
 
0.4%
creator_120 113
 
0.4%
creator_013 113
 
0.4%
creator_223 113
 
0.4%
creator_164 112
 
0.4%
Other values (290) 27104
96.0%
2025-12-27T18:34:27.742192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 56472
18.2%
c 28236
9.1%
e 28236
9.1%
a 28236
9.1%
t 28236
9.1%
o 28236
9.1%
_ 28236
9.1%
2 15325
 
4.9%
1 15111
 
4.9%
0 14862
 
4.8%
Other values (7) 39410
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197652
63.6%
Decimal Number 84708
27.3%
Connector Punctuation 28236
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15325
18.1%
1 15111
17.8%
0 14862
17.5%
3 5737
 
6.8%
9 5709
 
6.7%
6 5667
 
6.7%
5 5644
 
6.7%
8 5630
 
6.6%
7 5520
 
6.5%
4 5503
 
6.5%
Lowercase Letter
ValueCountFrequency (%)
r 56472
28.6%
c 28236
14.3%
e 28236
14.3%
a 28236
14.3%
t 28236
14.3%
o 28236
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 28236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 197652
63.6%
Common 112944
36.4%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 28236
25.0%
2 15325
13.6%
1 15111
13.4%
0 14862
13.2%
3 5737
 
5.1%
9 5709
 
5.1%
6 5667
 
5.0%
5 5644
 
5.0%
8 5630
 
5.0%
7 5520
 
4.9%
Latin
ValueCountFrequency (%)
r 56472
28.6%
c 28236
14.3%
e 28236
14.3%
a 28236
14.3%
t 28236
14.3%
o 28236
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 310596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 56472
18.2%
c 28236
9.1%
e 28236
9.1%
a 28236
9.1%
t 28236
9.1%
o 28236
9.1%
_ 28236
9.1%
2 15325
 
4.9%
1 15111
 
4.9%
0 14862
 
4.8%
Other values (7) 39410
12.7%

followers_count_before_post
Real number (ℝ)

High correlation 

Distinct24347
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54126.576
Minimum5000
Maximum165591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:27.827264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile12391.75
Q130672.75
median46936.5
Q368475.25
95-th percentile126044
Maximum165591
Range160591
Interquartile range (IQR)37802.5

Descriptive statistics

Standard deviation33246.39
Coefficient of variation (CV)0.61423413
Kurtosis0.47962318
Mean54126.576
Median Absolute Deviation (MAD)18272
Skewness1.0013824
Sum1.528318 × 109
Variance1.1053225 × 109
MonotonicityNot monotonic
2025-12-27T18:34:27.930186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 90
 
0.3%
15000 65
 
0.2%
30000 62
 
0.2%
60000 30
 
0.1%
100000 28
 
0.1%
38893 4
 
< 0.1%
51053 4
 
< 0.1%
43790 4
 
< 0.1%
54526 4
 
< 0.1%
35347 4
 
< 0.1%
Other values (24337) 27941
99.0%
ValueCountFrequency (%)
5000 90
0.3%
5007 1
 
< 0.1%
5011 1
 
< 0.1%
5012 1
 
< 0.1%
5013 1
 
< 0.1%
5018 1
 
< 0.1%
5031 1
 
< 0.1%
5037 1
 
< 0.1%
5047 1
 
< 0.1%
5074 1
 
< 0.1%
ValueCountFrequency (%)
165591 1
< 0.1%
165070 1
< 0.1%
165000 1
< 0.1%
164652 1
< 0.1%
163712 1
< 0.1%
162919 1
< 0.1%
162663 1
< 0.1%
162516 1
< 0.1%
162038 1
< 0.1%
161384 1
< 0.1%

engagement_rate
Real number (ℝ)

High correlation 

Distinct27954
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2793771
Minimum0.055498861
Maximum21.070199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:28.044217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.055498861
5-th percentile0.40863036
Q11.1629225
median2.2162619
Q33.9690757
95-th percentile10.026687
Maximum21.070199
Range21.0147
Interquartile range (IQR)2.8061531

Descriptive statistics

Standard deviation3.5170442
Coefficient of variation (CV)1.0724732
Kurtosis9.0744043
Mean3.2793771
Median Absolute Deviation (MAD)1.2490315
Skewness2.7353563
Sum92596.493
Variance12.3696
MonotonicityNot monotonic
2025-12-27T18:34:28.138992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.07019931 283
 
1.0%
2.698758164 1
 
< 0.1%
2.21431915 1
 
< 0.1%
3.031728835 1
 
< 0.1%
1.6501105 1
 
< 0.1%
1.415171879 1
 
< 0.1%
1.949481701 1
 
< 0.1%
1.838752047 1
 
< 0.1%
0.551111048 1
 
< 0.1%
0.7748799596 1
 
< 0.1%
Other values (27944) 27944
99.0%
ValueCountFrequency (%)
0.05549886118 1
< 0.1%
0.05844464515 1
< 0.1%
0.06048152056 1
< 0.1%
0.06680675873 1
< 0.1%
0.06720189688 1
< 0.1%
0.07165568495 1
< 0.1%
0.0765918876 1
< 0.1%
0.07785253064 1
< 0.1%
0.08109450438 1
< 0.1%
0.08206871434 1
< 0.1%
ValueCountFrequency (%)
21.07019931 283
1.0%
21.0685624 1
 
< 0.1%
21.05404384 1
 
< 0.1%
21.05316995 1
 
< 0.1%
21.02699773 1
 
< 0.1%
21.02656546 1
 
< 0.1%
21.00823402 1
 
< 0.1%
21.00453878 1
 
< 0.1%
20.95667082 1
 
< 0.1%
20.9330855 1
 
< 0.1%

post_hour
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
9
28236 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28236
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 28236
100.0%

Length

2025-12-27T18:34:28.220929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T18:34:28.265034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9 28236
100.0%

Most occurring characters

ValueCountFrequency (%)
9 28236
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28236
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 28236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28236
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 28236
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 28236
100.0%

post_day
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Thursday
4103 
Tuesday
4102 
Wednesday
4069 
Monday
4041 
Saturday
3992 
Other values (2)
7929 

Length

Max length9
Median length8
Mean length7.1509775
Min length6

Characters and Unicode

Total characters201915
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowSaturday
3rd rowFriday
4th rowThursday
5th rowFriday

Common Values

ValueCountFrequency (%)
Thursday 4103
14.5%
Tuesday 4102
14.5%
Wednesday 4069
14.4%
Monday 4041
14.3%
Saturday 3992
14.1%
Sunday 3970
14.1%
Friday 3959
14.0%

Length

2025-12-27T18:34:28.323929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T18:34:28.404097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
thursday 4103
14.5%
tuesday 4102
14.5%
wednesday 4069
14.4%
monday 4041
14.3%
saturday 3992
14.1%
sunday 3970
14.1%
friday 3959
14.0%

Most occurring characters

ValueCountFrequency (%)
d 32305
16.0%
a 32228
16.0%
y 28236
14.0%
u 16167
8.0%
s 12274
 
6.1%
e 12240
 
6.1%
n 12080
 
6.0%
r 12054
 
6.0%
T 8205
 
4.1%
S 7962
 
3.9%
Other values (7) 28164
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 173679
86.0%
Uppercase Letter 28236
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 32305
18.6%
a 32228
18.6%
y 28236
16.3%
u 16167
9.3%
s 12274
 
7.1%
e 12240
 
7.0%
n 12080
 
7.0%
r 12054
 
6.9%
h 4103
 
2.4%
o 4041
 
2.3%
Other values (2) 7951
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
T 8205
29.1%
S 7962
28.2%
W 4069
14.4%
M 4041
14.3%
F 3959
14.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 201915
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 32305
16.0%
a 32228
16.0%
y 28236
14.0%
u 16167
8.0%
s 12274
 
6.1%
e 12240
 
6.1%
n 12080
 
6.0%
r 12054
 
6.0%
T 8205
 
4.1%
S 7962
 
3.9%
Other values (7) 28164
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 32305
16.0%
a 32228
16.0%
y 28236
14.0%
u 16167
8.0%
s 12274
 
6.1%
e 12240
 
6.1%
n 12080
 
6.0%
r 12054
 
6.0%
T 8205
 
4.1%
S 7962
 
3.9%
Other values (7) 28164
13.9%

is_weekend
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.2 KiB
False
20274 
True
7962 
ValueCountFrequency (%)
False 20274
71.8%
True 7962
 
28.2%
2025-12-27T18:34:28.487685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

has_high_hashtags
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.2 KiB
True
18917 
False
9319 
ValueCountFrequency (%)
True 18917
67.0%
False 9319
33.0%
2025-12-27T18:34:28.538683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

long_caption
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.2 KiB
True
26299 
False
 
1937
ValueCountFrequency (%)
True 26299
93.1%
False 1937
 
6.9%
2025-12-27T18:34:28.574680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

high_intent_engagement
Real number (ℝ)

High correlation 

Distinct13962
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9977.1642
Minimum124
Maximum19965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.2 KiB
2025-12-27T18:34:28.642853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum124
5-th percentile2676
Q16134.75
median9993.5
Q313751.5
95-th percentile17299.25
Maximum19965
Range19841
Interquartile range (IQR)7616.75

Descriptive statistics

Standard deviation4595.4393
Coefficient of variation (CV)0.46059574
Kurtosis-0.99778728
Mean9977.1642
Median Absolute Deviation (MAD)3800.5
Skewness0.0063425592
Sum2.8171521 × 108
Variance21118063
MonotonicityNot monotonic
2025-12-27T18:34:28.747502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11582 9
 
< 0.1%
14476 9
 
< 0.1%
8893 9
 
< 0.1%
5451 8
 
< 0.1%
8496 8
 
< 0.1%
10479 8
 
< 0.1%
12395 8
 
< 0.1%
15735 8
 
< 0.1%
6581 8
 
< 0.1%
10144 7
 
< 0.1%
Other values (13952) 28154
99.7%
ValueCountFrequency (%)
124 1
< 0.1%
131 1
< 0.1%
138 1
< 0.1%
139 1
< 0.1%
178 1
< 0.1%
180 1
< 0.1%
189 1
< 0.1%
199 1
< 0.1%
200 1
< 0.1%
253 1
< 0.1%
ValueCountFrequency (%)
19965 1
< 0.1%
19933 1
< 0.1%
19925 1
< 0.1%
19881 1
< 0.1%
19872 1
< 0.1%
19842 1
< 0.1%
19808 1
< 0.1%
19800 1
< 0.1%
19792 1
< 0.1%
19781 1
< 0.1%

follower_bucket
Categorical

High correlation  Uniform 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.6 KiB
Small
7059 
Medium
7059 
Large
7059 
Very Large
7059 

Length

Max length10
Median length8
Mean length6.5
Min length5

Characters and Unicode

Total characters183534
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLarge
2nd rowSmall
3rd rowVery Large
4th rowVery Large
5th rowVery Large

Common Values

ValueCountFrequency (%)
Small 7059
25.0%
Medium 7059
25.0%
Large 7059
25.0%
Very Large 7059
25.0%

Length

2025-12-27T18:34:28.847650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T18:34:28.911242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
large 14118
40.0%
small 7059
20.0%
medium 7059
20.0%
very 7059
20.0%

Most occurring characters

ValueCountFrequency (%)
e 28236
15.4%
a 21177
11.5%
r 21177
11.5%
m 14118
 
7.7%
l 14118
 
7.7%
L 14118
 
7.7%
g 14118
 
7.7%
S 7059
 
3.8%
M 7059
 
3.8%
d 7059
 
3.8%
Other values (5) 35295
19.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 141180
76.9%
Uppercase Letter 35295
 
19.2%
Space Separator 7059
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28236
20.0%
a 21177
15.0%
r 21177
15.0%
m 14118
10.0%
l 14118
10.0%
g 14118
10.0%
d 7059
 
5.0%
i 7059
 
5.0%
u 7059
 
5.0%
y 7059
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
L 14118
40.0%
S 7059
20.0%
M 7059
20.0%
V 7059
20.0%
Space Separator
ValueCountFrequency (%)
7059
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 176475
96.2%
Common 7059
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28236
16.0%
a 21177
12.0%
r 21177
12.0%
m 14118
8.0%
l 14118
8.0%
L 14118
8.0%
g 14118
8.0%
S 7059
 
4.0%
M 7059
 
4.0%
d 7059
 
4.0%
Other values (4) 28236
16.0%
Common
ValueCountFrequency (%)
7059
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 183534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 28236
15.4%
a 21177
11.5%
r 21177
11.5%
m 14118
 
7.7%
l 14118
 
7.7%
L 14118
 
7.7%
g 14118
 
7.7%
S 7059
 
3.8%
M 7059
 
3.8%
d 7059
 
3.8%
Other values (5) 35295
19.2%

Interactions

2025-12-27T18:34:18.614609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-27T18:34:15.344131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:16.400327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:18.261706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:19.808765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:06.464636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:07.464481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:08.473434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:09.501977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:11.023066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:11.939131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:13.197980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:14.289671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:15.428872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:16.546175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:18.383250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:19.883950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:06.560330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:07.533366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:08.555476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:09.574597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:11.095507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:12.051978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:13.286475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:14.397136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:15.496325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:16.661071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-27T18:34:18.487184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-27T18:34:29.006798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
caption_lengthcommentscontent_categoryengagement_ratefollower_bucketfollowers_count_before_postfollowers_gainedhas_high_hashtagshashtags_counthigh_intent_engagementimpressionsis_weekendlikeslong_captionmedia_typepost_dayreachsavessharestraffic_source
caption_length1.000-0.0090.005-0.0020.0080.002-0.0020.0000.0040.010-0.0060.0080.0010.8090.0000.000-0.0050.0100.0030.000
comments-0.0091.0000.0030.0320.0000.0050.0020.0060.008-0.0040.0020.000-0.0020.0120.0120.000-0.000-0.004-0.0060.000
content_category0.0050.0031.0000.0000.0000.0040.0060.0140.0070.0000.0090.0080.0040.0160.0080.0000.0080.0070.0000.004
engagement_rate-0.0020.0320.0001.0000.466-0.6890.0010.003-0.0040.0610.0360.0000.6710.0000.0000.0000.0400.0570.0260.000
follower_bucket0.0080.0000.0000.4661.0000.8340.0080.0060.0110.0060.0000.0000.0000.0030.0050.0000.0000.0070.0000.000
followers_count_before_post0.0020.0050.004-0.6890.8341.000-0.0050.0000.004-0.008-0.0020.0070.0010.0000.0000.004-0.003-0.008-0.0040.010
followers_gained-0.0020.0020.0060.0010.008-0.0051.0000.0040.0080.002-0.0000.008-0.0060.0020.0010.0000.0000.0000.0050.009
has_high_hashtags0.0000.0060.0140.0030.0060.0000.0041.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.009
hashtags_count0.0040.0080.007-0.0040.0110.0040.0081.0001.000-0.003-0.0040.002-0.0010.0000.0000.009-0.006-0.001-0.0050.000
high_intent_engagement0.010-0.0040.0000.0610.006-0.0080.0020.000-0.0031.0000.0050.000-0.0010.0000.0060.0070.0050.9520.3040.000
impressions-0.0060.0020.0090.0360.000-0.002-0.0000.000-0.0040.0051.0000.0090.0490.0080.0000.0080.9690.008-0.0080.000
is_weekend0.0080.0000.0080.0000.0000.0070.0080.0000.0020.0000.0091.0000.0080.0000.0001.0000.0000.0040.0000.000
likes0.001-0.0020.0040.6710.0000.001-0.0060.000-0.001-0.0010.0490.0081.0000.0000.0000.0030.054-0.0030.0060.000
long_caption0.8090.0120.0160.0000.0030.0000.0020.0000.0000.0000.0080.0000.0001.0000.0000.0000.0040.0180.0000.000
media_type0.0000.0120.0080.0000.0050.0000.0010.0000.0000.0060.0000.0000.0000.0001.0000.0000.0110.0000.0000.002
post_day0.0000.0000.0000.0000.0000.0040.0000.0000.0090.0070.0081.0000.0030.0000.0001.0000.0070.0070.0000.000
reach-0.005-0.0000.0080.0400.000-0.0030.0000.000-0.0060.0050.9690.0000.0540.0040.0110.0071.0000.007-0.0060.000
saves0.010-0.0040.0070.0570.007-0.0080.0000.000-0.0010.9520.0080.004-0.0030.0180.0000.0070.0071.0000.0050.000
shares0.003-0.0060.0000.0260.000-0.0040.0050.000-0.0050.304-0.0080.0000.0060.0000.0000.000-0.0060.0051.0000.000
traffic_source0.0000.0000.0040.0000.0000.0100.0090.0090.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0001.000

Missing values

2025-12-27T18:34:20.030062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-27T18:34:20.218864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

post_idupload_datemedia_typelikescommentssharessavesreachimpressionscaption_lengthhashtags_countfollowers_gainedtraffic_sourcecontent_categoryaccount_idfollowers_count_before_postengagement_ratepost_hourpost_dayis_weekendhas_high_hashtagslong_captionhigh_intent_engagementfollower_bucket
1IG00134022025-04-04 09:25:22.954916Reel1324205003366756011546640201680217506171Reels FeedTechnologycreator_135543552.6987589FridayFalseFalseTrue9268Large
2IG00107512025-04-19 09:25:22.954916Video31476675443821289119169332079234150226892HashtagsPhotographycreator_109267632.0738719SaturdayTrueTrueTrue17273Small
3IG00133612025-08-15 09:25:22.954916Video9455697501667109651451049181626862627530HashtagsFashioncreator_135714571.6364819FridayFalseTrueTrue12632Very Large
4IG00270342025-02-27 09:25:22.954916Video3810517434306234112405981707533113116289ExploreTechnologycreator_2711199680.3875629ThursdayFalseTrueTrue6647Very Large
5IG00222122025-09-05 09:25:22.954916Carousel11314194773079310496673690055264604ProfilePhotographycreator_223768061.6405889FridayFalseFalseFalse3389Very Large
6IG00270622024-11-25 09:25:22.954916Video39301959776336922244969207144725295Reels FeedPhotographycreator_2711025130.4880369MondayFalseTrueTrue1132Very Large
8IG00215892025-09-03 09:25:22.954916Photo60627274733714244841674860179139712550ExternalTechnologycreator_218782770.9068959WednesdayFalseTrueTrue7615Very Large
9IG00127682025-03-30 09:25:22.954916Video17177680053337837163799820559988371216Home FeedFitnesscreator_129271576.7737609SundayTrueTrueTrue4174Small
10IG00002812024-11-20 09:25:22.954916Reel1984919045307924948676991035039697259Home FeedLifestylecreator_003602373.5378429WednesdayFalseFalseTrue5573Large
11IG00131752025-08-29 09:25:22.954916Reel148808937825263553667898769902429229HashtagsBeautycreator_133664032.4737599FridayFalseFalseFalse6079Large
post_idupload_datemedia_typelikescommentssharessavesreachimpressionscaption_lengthhashtags_countfollowers_gainedtraffic_sourcecontent_categoryaccount_idfollowers_count_before_postengagement_ratepost_hourpost_dayis_weekendhas_high_hashtagslong_captionhigh_intent_engagementfollower_bucket
29989IG00282992025-02-11 09:25:22.954916Photo18891591341882125751802319219291856422263Home FeedTechnologycreator_284430764.9332819TuesdayFalseTrueTrue14457Medium
29990IG00136012025-09-30 09:25:22.954916Reel1273916906170083091004168108497699218124ExternalTravelcreator_137845751.7062499TuesdayFalseTrueTrue10009Very Large
29991IG00298522025-02-14 09:25:22.954916Reel591631247416476660156310023911210732ExploreFoodcreator_299183413.5625109FridayFalseFalseFalse4930Small
29992IG00087462025-11-19 09:25:22.954916Reel505192735358372461792483202416221121737ExploreFashioncreator_090901260.7110389WednesdayFalseFalseTrue10829Very Large
29993IG00230222025-11-17 09:25:22.954916Reel17940081119452002157949218017851761351Home FeedComedycreator_2311581251.1646369MondayFalseTrueTrue3947Very Large
29994IG00196532024-12-12 09:25:22.954916Carousel1377026968284836341447382187382827116430Reels FeedFashioncreator_198658982.2937279ThursdayFalseTrueTrue6482Large
29995IG00130792024-12-12 09:25:22.954916Carousel150118694310847879773511229434150618300ExternalBeautycreator_132990216.3558889ThursdayFalseTrueTrue4895Small
29996IG00016982025-07-21 09:25:22.954916Reel6613127892410604810908671561926195625178ExploreFitnesscreator_017850140.9101799MondayFalseTrueTrue8458Very Large
29997IG00109592025-03-19 09:25:22.954916Reel81369782255214862449706850597851978Reels FeedFoodcreator_111260574.0144689WednesdayFalseTrueFalse15414Small
29998IG00240842025-10-07 09:25:22.954916Video75694869724856541402361644327132326960Reels FeedTravelcreator_242590441.5821599TuesdayFalseTrueTrue9026Large